Book Image

Python Web Scraping - Second Edition

By : Katharine Jarmul
Book Image

Python Web Scraping - Second Edition

By: Katharine Jarmul

Overview of this book

The Internet contains the most useful set of data ever assembled, most of which is publicly accessible for free. However, this data is not easily usable. It is embedded within the structure and style of websites and needs to be carefully extracted. Web scraping is becoming increasingly useful as a means to gather and make sense of the wealth of information available online. This book is the ultimate guide to using the latest features of Python 3.x to scrape data from websites. In the early chapters, you'll see how to extract data from static web pages. You'll learn to use caching with databases and files to save time and manage the load on servers. After covering the basics, you'll get hands-on practice building a more sophisticated crawler using browsers, crawlers, and concurrent scrapers. You'll determine when and how to scrape data from a JavaScript-dependent website using PyQt and Selenium. You'll get a better understanding of how to submit forms on complex websites protected by CAPTCHA. You'll find out how to automate these actions with Python packages such as mechanize. You'll also learn how to create class-based scrapers with Scrapy libraries and implement your learning on real websites. By the end of the book, you will have explored testing websites with scrapers, remote scraping, best practices, working with images, and many other relevant topics.
Table of Contents (10 chapters)

Comparing performance

To help evaluate the trade-offs between the three scraping approaches described in the section, Three approaches to scrape a web page, it would be helpful to compare their relative efficiency. Typically, a scraper would extract multiple fields from a web page. So, for a more realistic comparison, we will implement extended versions of each scraper which extract all the available data from a country's web page. To get started, we need to return to our browser to check the format of the other country features, as shown here:

By using our browser's inspect capabilities, we can see each table row has an ID starting with places_ and ending with __row. The country data is contained within these rows in the same format as the area example. Here are implementations that use this information to extract all of the available country data:

FIELDS = ('area', 'population',...